To categorize is to respond differently to objects or events in separate classes or categories. This vitally important skill allows people to approach food or friend and to avoid toxin or trap. Recent evidence suggests that human category learning is mediated by multiple, qualitatively distinct learning systems, and much is now known about the neurobiology that underlies these systems. Most of this evidence comes from artificial tasks that were specially designed to load on only one system. As a result, almost nothing is known about how these various systems interact and about how their separate contributions are coordinated. This is especially important because it seems likely that a number of systems would contribute in most real-life category-learning situations. Another critical difference between real life and most laboratory studies of category learning is that in real life people become experts at certain types of categorization. The question of how expertise develops is especially important because there is good evidence that the neural mechanisms and pathways that mediate the learning of new categories are different from the neural structures that mediate the representation of highly learned categories. This project is a continuation of a research program that provided much of the evidence for multiple systems, and that discovered many unique properties of the component systems. The current project has two goals. The first is to understand how learning in the various category-learning systems is coordinated and the second is to understand how categorization expertise develops. These problems will be attacked using a number of approaches, including traditional cognitive experiments, studies with Huntington's disease patients, fMRI experiments, and neuro-computational modeling. The results will improve understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of different neurological disorders, and suggest improvements in training procedures for complex categorization tasks (e.g., teaching radiologists to find tumors in x-rays).